To perform the layer-wise behavior of various DNN models in heterogeneous deployment platforms, Xia et al. in [1]} proposed a DNN tuning framework. This work provides a detailed discussion of the performance and energy consumption of DNN models (i.e., CNN, LSTM, MLP) concerning different deployment strategies (i.e., cloud, mobile device, mobile-cloud hybrid). Similarly, Turner et al. in [2]} proposed various mainstream DNN compression methods with the evaluation of the model accuracy, training time, and memory on two types of execution hardware (i.e., CPU, GPU). In [3]}, Bianco et al. performed an extensive system-level analysis of a wide range of DNN models on two different computing architectures, namely NVIDIA Titan X Pascal and NVIDIA Jetson TX1. From an intuitively different perspective, the authors of [4]} represent a detailed analysis of DNN-based application execution in optimized hardware and their overhead considering various prior and post operations during implementation on a special-purpose accelerator. This work proposes to utilize a specialized edge data center designed for DNN-based application that overcomes the data processing overhead. On the contrary, our work focuses on inference time behavior of DNN-based applications on heterogeneous cloud machine types, especially in the smart O&G industry.
